EÌvaluation des risques que comporte lâexposition aux gras trans au Canada
Bibliographic record
Abstract
Les gras trans sont indeÌsirables, car ils augmentent la concentration sanguine en cholesteÌrol aÌ lipoproteÌines de faible densiteÌ et abaissent celle en cholesteÌrol aÌ lipoproteÌines de haute densiteÌ, ce qui peut mener aÌ un risque accru de coronaropathie. Au milieu des anneÌes 1990, les chercheurs ont estimeÌ quʹau sein de la population canadienne les apports en gras trans comptaient au nombre des plus eÌleveÌs au monde en atteignant environ 3,7 % de lâapport eÌnergeÌtique. Selon la recommandation de lâOrganisation mondiale de la santeÌ, lâapport moyen en gras trans devrait eÌtre infeÌrieur aÌ 1 % de lâapport eÌnergeÌtique total. Par conseÌquent, le Canada a adopteÌ une deÌmarche aÌ volets multiples visant aÌ diminuer la teneur en gras trans des aliments canadiens. Les initiatives entreprises sont les suivantes : lâeÌtiquetage nutritionnel obligatoire, lâeÌtablissement dʹun groupe de travail multilateÌral, soit le Groupe dâeÌtude sur les graisses trans, dans le but dâeÌlaborer des recommandations et des strateÌgies ciblant lâeÌlimination des gras trans dans lâapprovisionnement alimentaire canadien et, plus reÌcemment, la surveillance des efforts investis par lâindustrie dans la diminution des gras trans contenus dans ses produits. De concert, ces initiatives ont eÌteÌ couronneÌes de succeÌs alors que lâapport moyen en gras trans a atteint 1,42% de lâapport eÌnergeÌtique total. Dâautres reÌductions de la teneur en gras trans de lâapprovisionnement alimentaire au Canada sont essentielles pour atteindre pleinement la cible de 1 % de lâapport eÌnergeÌtique ainsi que les objectifs affeÌrents en matieÌre de santeÌ publique et de protection des populations vulneÌrables.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".